Sci/Tech

Academics generate enormous amounts of software, some of which inspires commercial innovations in networking and other areas. But little academic software gets released to the public and even less enters common use. Is some vast "dark matter" being overlooked in the academic community? Would the world benefit from academics turning more of their software into free and open projects?

I asked myself these questions a few months ago when Red Hat, at its opening of a new innovation center in Boston's high-tech Fort Point neighborhood, announced a unique partnership with the goal of tapping academia. Red Hat is joining with Boston-area computer science departments—starting with Boston University—to identify promising software developed in academic projects and to turn it into viable free-software projects. Because all software released by Red Hat is under free licenses, the partnership suggests a new channel by which academic software could find wider use.

A culture of transparency permeates the Dataverse project, contributing to its adoption in dozens of research institutions around the world. Headquartered at Harvard University, the Dataverse development team has more than a decade of experience operating as an open source project within an organization that values transparency: the Institute of Quantitative Social Science (IQSS). Working transparently helps the Dataverse team communicate changes to current development efforts, provides opportunities for the community to support each other, and facilitates contribution to the project.

Here's an open invitation to steal. It goes out to cancer fighters and tempts them with a new program that predicts cancer drug effectiveness via machine learning and raw genetic data.

The researchers who built the program at the Georgia Institute of Technology would like cancer fighters to take it for free, or even just swipe parts of their programming code, so they've made it open source. They hope to attract a crowd of researchers who will also share their own cancer and computer expertise and data to improve upon the program and save more lives together.

A team of researchers from Atlanta-based Georgia Institute of Technology introduced an open-source algorithm Oct. 26 that predicts a cancer drug's effectiveness based on a patient's genetic data.

The researchers developed the machine learning algorithm using gene expression and drug response data from the National Cancer Institute's panel of 60 human cancer cell lines. Their goal was to create an algorithm that predicts optimal drug therapies based on individual patient tumors.

In a study of 273 cancer patients, researchers found the algorithm to be about 85 percent accurate in assessing the effectiveness of nine drugs. By releasing the algorithm on an open-source platform, they hope other researchers will participate in refining their work.

Surprisingly, the MXNet Machine Learning project was this month accepted by the Apache Software Foundation as an open-source project.

What's surprising about the announcement isn't so much that the ASF is accepting this face in the crowd to its ranks – it's hard to turn around in the software world these days without tripping over ML tools – but rather that MXNet developers, most of whom are from Amazon, believe ASF is relevant.

During the past decade, enterprises have begun using machine learning (ML) to collect and analyze large amounts of data to obtain a competitive advantage. Now some are looking to go even deeper – using a subset of machine learning techniques called deep learning (DL), they are seeking to delve into the more esoteric properties hidden in the data. The goal is to create predictive applications for such areas as fraud detection, demand forecasting, click prediction, and other data-intensive analyses.

Machine learning has become a buzzword. A branch of Artificial Intelligence, it adds marketing sparkle to everything from intrusion detection tools to business analytics. What is it, exactly, and how can you code it?

Dropbox has released the code for the chatbot it uses to question employees about interactions with corporate systems, in the hope that it can help other organizations automate security processes and improve employee awareness of security concerns.

"One of the hardest, most time-consuming parts of security monitoring is manually reaching out to employees to confirm their actions," said Alex Bertsch, formerly a Dropbox intern and now a teaching assistant at Brown University, in a blog post. "Despite already spending a significant amount of time on reach-outs, there were still alerts that we didn't have time to follow up on."

After two Release Candidate (RC) development builds, the final version of the Scientific Linux 7.3 operating system arrived today, January 26, 2017, as announced by developer Pat Riehecky.

Derived from the freely distributed sources of the commercial Red Hat Enterprise Linux 7.3 operating system, Scientific Linux 7.3 includes many updated components and all the GNU/Linux/Open Source technologies from the upstream release.

Of course, all of Red Hat Enterprise Linux's specific packages have been removed from Scientific Linux, which now supports Scientific Linux Contexts, allowing users to create local customization for their computing needs much more efficiently than before.

When programmers at the MIT Instrumentation Laboratory set out to develop the flight software for the Apollo 11 space program in the mid-1960s, the necessary technology did not exist. They had to invent it.

They came up with a new way to store computer programs, called “rope memory,” and created a special version of the assembly programming language. Assembly itself is obscure to many of today’s programmers—it’s very difficult to read, intended to be easily understood by computers, not humans. For the Apollo Guidance Computer (AGC), MIT programmers wrote thousands of lines of that esoteric code.